Tarun Nagar on Selling Agentic AI Development Services to Sceptical Enterprise Decision-Makers
By Aarti Jangid 24-07-2026 3
Agentic AI is everywhere in vendor decks right now, but inside most enterprises the reaction is still caution rather than excitement. Budget owners have sat through enough overhyped pilots to be wary of another one, and the term "agent" itself gets thrown around loosely enough that most buying committees aren't sure what they're actually being asked to approve. We sat down with Tarun Nagar, Founder & CEO of Dev Technosys, to talk about what actually convinces a skeptical buying committee to greenlight an agentic AI project — and why that conversation looks nothing like a typical software pitch.
Q.You talk to enterprise buyers every week. What's the first objection you hear before they'll even consider agentic AI?
Almost always, it's some version of "we tried a chatbot two years ago and it didn't work." People conflate agentic systems with the scripted bots that frustrated everyone in 2019. I spend the first ten minutes of most calls just separating general artificial intelligence hype from what we actually build. We're not selling general AI that reasons like a person across every domain — we're selling a system that can reliably execute a defined set of business tasks. That distinction alone defuses half the skepticism. The rest usually gets resolved once we walk them through real Artificial Intelligence Consulting Services — sitting with their team, mapping which workflows are worth automating, before anyone writes a line of code.
Q.How do you explain what an agent in artificial intelligence actually is to someone without a technical background?
I tell them an agent in artificial intelligence is software that can plan, use tools, and check its own work across several steps — not just answer one question and stop. A traditional chatbot responds; an agent acts. It can pull a customer record, cross-reference a policy document, and draft a response, all without a human clicking through each step. That's the piece most buyers haven't seen yet, and once they see it live, the conversation shifts from "is this real" to "where else can we use this."
Q.Enterprise employees already have access to ChatGPT. Why do they need custom AI development services on top of that?
This comes up constantly. A general-purpose chat gpt alternative account is fine for drafting an email, but it isn't connected to your CRM, your compliance rules, or your internal data — and it shouldn't be, from a governance standpoint. Real Artificial Intelligence Development Services mean building something that respects data boundaries, logs every action, and plugs into systems your team already uses. A good example is natural language processing in customer service: a public model can draft a nice-sounding reply, but it can't see the customer's order history or refund policy unless someone builds that connection properly, with the right guardrails.
Q. Security and compliance come up a lot, especially in regulated industries. How do you handle that?
Finance is the sharpest example. AI in finance covers fraud detection, credit decisioning, and reconciliation — all places where a mistake is expensive and auditors will ask questions. That's exactly why ai consulting services has to come before ai development services, not after. We map data flows, flag what can't leave a client's environment, and design the agent's permissions before training begins. Skeptical CFOs relax considerably once they see that the conversation starts with risk, not with a demo.
Q. Do your own engineers push back on agentic tools, worried they'll be replaced?
Less than you'd expect, because they're already living this shift personally. Developers are actively comparing Cursor vs Claude Code in their own editors, deciding which AI copilot actually earns a place in their workflow. That same instinct — try it, measure it, keep what earns trust — is what we bring into AI Copilot Development Services for clients. We're not asking enterprise teams to accept agentic AI on faith. We're asking them to evaluate it the same way a good engineer already evaluates any new tool.
Q. Do consumer AI trends help or hurt when you're explaining this to executives?
They help, if you use them right. Everyone's watched generative AI tools mature fast — Stable Diffusion artificial intelligence models went from novelty to production-grade image generation in a couple of years. Even something like the demand for a Character AI alternative, or teenagers finding Character AI unblocked on a school network, shows how fast adoption outruns policy. I use that as a warning, not a selling point: if your organization doesn't have a clear AI policy, employees will find workarounds anyway. Better to build technological AI into the workflow deliberately than to have it show up unofficially.
Q. How do you frame ROI for a skeptical finance team?
I avoid vague productivity claims. We talk in terms of time and cost arbitrage — the same logic behind ai arbitrage strategies, where speed and accuracy differences translate directly into margin. If an agent cuts a four-hour reconciliation process to twenty minutes, that's a number a CFO can put in a spreadsheet, not a slide.
Q. This field moves fast. How do you stay current enough to advise clients confidently?
Honestly, checking the latest AI news today is part of the job now, the same way checking market data is for a trader. My team tracks model releases, new agent frameworks, and Machine Learning Development research weekly, and we re-test our own recommendations against whatever changes. Advising on AI technology without doing that would be irresponsible.
Final advice for other leaders trying to get an agentic AI project approved internally?
Start small, start honest. Don't promise general AI magic — promise a defined task done reliably, with a clear before-and-after number. Bring in Artificial Intelligence Consulting Services before you commit budget to a full build. Skeptical decision-makers aren't wrong to be cautious; they're just waiting for someone to show them the risk was actually considered, not just the upside. Win that trust once, on one workflow, and the next approval gets a lot easier to get across the table.
Tags : agentic ai development
